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Economical and Ecological High Quality Painting at Highly Scalable Batch Sizes


European industry is in a transition towards a more competitive and knowledge-based one. Production on demand, mass customisation, rapid reaction to market changes and time-to-market of new products at low cost and high quality are needed for this transition. Current systems are insufficient due to large efforts and costs for adapting to new products. ECO2PAINTER targets a flexible high quality robotic painting system with minimal configuration-time at production changes.

Within this project, a system will be developed that scans the product to be painted and localizes, recognizes and decomposes the object and its process-relevant regions. Then the painting process and the robot motions are automatically planned and simulated. Quality improvements are done via intuitive user interaction, automatic stroke adaptation and learning and feedback to the planner. If the process criteria are met, the automatic generated programs are executed on the paint robots. Improvements are transferred toward new variants and products. Results will be ready to market before new standards on emissions require updates of existing systems. The increased competitiveness of the pioneered system will improve the ROI. The major innovation is clearly the novel system that can efficiently paint goods even at small batch sizes at very high quality, as common in products for mass customisation. Innovations needed: -Robust adaptive and active controlled 3D sensing, recognition and categorization, able to deal with large uncertainties, range of objects and complex shapes. 'Closed loop' process and motion planning, application simulation and adaptation for increased satisfaction of quality criteria -Intuitive interaction via augmented application-animation, automatic adaptation and learning that enable the use of optimisation and knowledge even at new product variants
-Efficient interfaces for non-robotic experts
-Improved paint stroke scheduler increases the efficiency of robotic painting

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Im Stadtgut A2

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Participants (7)